NDLI: Automatic creation of photo books from stories in social media

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Automatic creation of photo books from stories in social media

Content Provider	ACM Digital Library
Author	Boll, Susanne Rabbath, Mohamad Sandhaus, Philipp
Abstract	Photos are a special way to tell stories of our best memories. The representation of those photos in appealing physical photo books is highly appreciated by many people. Today, many photos are shared via social networking sites, where people upload their photos and share their stories with their social network. The members of the social network often comment on the photos, add tags or descriptions and upload photos for the same events in their albums. While the media of different personal events are now available on the social network, there is no easy way to collect and bundle them into some story and print the story into a photo book. We propose an approach to automatically detect media elements that match a query (where, when, what, who) in the user's social network and intelligently arrange and compose them into a printed photo book. We combine content analysis of text and images to automatically and semi-automatically select photos of a specific story. Photos that are highly probable to belong to the same event are clustered using an Expectation-Maximization algorithm to be able to easily retrieve them using online queries. People tags that are added to the photos are employed to select important photos by automatically calculating the individual social importance of friends. The selected content and derived semantics are employed to create an appealing layout for automatically created photo books.
Starting Page	15
Ending Page	20
Page Count	6
File Format	PDF
ISBN	9781450301732
DOI	10.1145/1878151.1878157
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Expectation-maximization Layout Photo books Social media Information retrieval Social networks Multimedia retrieval
Content Type	Text
Resource Type	Article

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